GARS
This is the released version of GARS; for the devel version, see GARS.
GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets
Bioconductor version: Release (3.23)
Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.
Author: Mattia Chiesa <mattia.chiesa at hotmail.it>, Luca Piacentini <luca.piacentini at cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa at hotmail.it>
citation("GARS")):
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M (2015). "Orchestrating high-throughput genomic analysis with Bioconductor." Nature Methods, 12(2), 115–121. doi:10.1038/nmeth.3252.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004). "Bioconductor: open software development for computational biology and bioinformatics." Genome Biology, 5(10), R80. doi:10.1186/gb-2004-5-10-r80.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GARS")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("GARS")| GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets | R Script | |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | Classification, Clustering, FeatureExtraction, Software |
| Version | 1.32.0 |
| In Bioconductor since | BioC 3.7 (R-3.5) (8.5 years) |
| License | GPL (>= 2) |
| Depends | R (>= 3.5), ggplot2, cluster |
| Imports | DaMiRseq, MLSeq, stats, methods, SummarizedExperiment |
| System Requirements | |
| URL |
See More
| Suggests | BiocStyle, knitr, testthat |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | GARS_1.32.0.tar.gz |
| Windows Binary (x86_64) | GARS_1.32.0.zip |
| macOS Binary (big-sur-x86_64) | GARS_1.32.0.tgz |
| macOS Binary (sonoma-arm64) | GARS_1.32.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/GARS |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/GARS |
| Bioc Package Browser | https://code.bioconductor.org/browse/GARS/ |
| Package Short Url | https://bioconductor.org/packages/GARS/ |
| Package Downloads Report | Download Stats |